LOCO: Distributing Ridge Regression with Random Projections

Software

CoRe: Conditional Variance Penalties and Domain Shift Robustness

TensorFlow implementation

TensorFlow implementation of 'CoRe' (COnditional Variance REgularization),
proposed in "Conditional Variance Penalties and Domain Shift Robustness".
The aim is to build classifiers that are robust against specific interventions.
These domain-shift interventions are defined in a causal graph, extending the
framework of Gong et al (2016). In contrast to Gong et al. we work on a
setting where the domain variable itself is latent but we can observe for
some instances a so-called identifier variables that indicates, for example,
presence of the same person or object across different images. Penalizing the
variance of the predictions across instances that share the same class label and
identifier leads to robustness against strong domain-shift interventions. Github.

nonlinearICP

R package

Code for 'nonlinear Invariant Causal Prediction' to estimate the
causal parents of a given target variable from data collected in
different experimental or environmental conditions, extending
'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016)
to nonlinear settings. Github.